Key result
Deep learning reconstruction with 80-kVp CCTA and 1.4 g/s IDR reduced radiation dose by 42% (2.36 vs 4.07 mSv; p<0.001) and improved image quality compared to standard 100-kVp ASiR-V.
Why the study?
Minimizing radiation and contrast medium dose while maintaining CT image quality in CCTA is highly desirable.
Does a double-low CCTA protocol with high-strength deep learning image reconstruction reduce radiation and contrast dose while maintaining image quality compared to standard ASiR-V protocol in non-obese patients?
RCT (n=255)
randomly assigned
Does a double-low CCTA protocol with high-strength deep learning image reconstruction reduce radiation and contrast dose while maintaining image quality compared to standard ASiR-V protocol in non-obese patients?
Effect estimate: 42% reduction
Absolute Event Rate: 2.36% vs 4.07%
p-value: p=<0.001
A double-low CCTA protocol using high-strength deep learning reconstruction significantly reduces radiation and contrast medium exposure while improving image quality in non-obese patients.
OBJECTIVE: To evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) protocol in non-obese patients. MATERIALS AND METHODS: were prospectively included and randomly assigned into three groups: group A (100 kVp, ASiR-V 50%, iodine delivery rate [IDR] = 1.8 g/s), group B (80 kVp, DLIR-H, IDR = 1.4 g/s), and group C (80 kVp, DLIR-H, IDR = 1.2 g/s). High-concentration contrast medium was administered. Image quality analysis was evaluated by two radiologists. Radiation and contrast dose, and objective and subjective image quality were compared across the three groups. RESULTS: The final population consisted of 255 patients (64 ± 10 years, 161 men), 85 per group. Group B yielded 42% radiation dose reduction (2.36 ± 0.9 mSv) compared to group A (4.07 ± 1.2 mSv; p < 0.001) and achieved a higher signal-to-noise ratio (30.5 ± 11.5), contrast-to-noise-ratio (27.8 ± 11), and subjective image quality (Likert scale score: 4, interquartile range: 3-4) compared to group A and group C (all p ≤ 0.001). Contrast medium dose in group C (44.8 ± 4.4 mL) was lower than group A (57.7 ± 6.2 mL) and B (50.4 ± 4.3 mL), all the comparisons were statistically different (all p < 0.001). CONCLUSION: DLIR-H combined with 80-kVp CCTA with an IDR 1.4 significantly reduces radiation and contrast medium exposure while improving image quality compared to conventional 100-kVp with 1.8 IDR protocol in non-obese patients. CLINICAL RELEVANCE STATEMENT: Low radiation and low contrast medium dose coronary CT angiography protocol is feasible with high-strength deep learning reconstruction and high-concentration contrast medium without compromising image quality. KEY POINTS: Minimizing the radiation and contrast medium dose while maintaining CT image quality is highly desirable. High-strength deep learning iterative reconstruction protocol yielded 42% radiation dose reduction compared to conventional protocol. "Double-low" coronary CTA is feasible with high-strength deep learning reconstruction without compromising image quality in non-obese patients.
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Caruso et al. (2024) conducted an RCT in Coronary computed tomography angiography in non-obese patients (n=255). Deep learning image reconstruction (DLIR-H) with 80 kVp vs. Standard adaptive statistical iterative reconstruction (ASiR-V) with 100 kVp and IDR 1.8 g/s was evaluated on Radiation dose (42% reduction, p=<0.001). Deep learning reconstruction with 80-kVp CCTA and 1.4 g/s IDR reduced radiation dose by 42% (2.36 vs 4.07 mSv; p<0.001) and improved image quality compared to standard 100-kVp ASiR-V.
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